Changes in the structure and hydration properties of high-temperature peanut protein induced by cold plasma oxidation.

To improve the hydration properties of high-temperature pressed peanut protein isolate (HPPI), we investigated the effect of cold plasma (CP) oxidation on functional and structural properties. Compared to HPPI, the hydrated molecules number and the surface contact angle were significantly decreased at 70 W, from 77.2 × 109 to 17.7 × 109 and from 85.74° to 57.81°, respectively. The reduction of the sulfhydryl content and the increase of the disulfide bond and di-tyrosine content indicated that the structural transformation was affected by the oxidation effect. In terms of structural changes, a stretched tertiary structure, ordered secondary structure, and rough apparent structure were observed after CP treatment. Additionally, the enhancement of surface free energy and group content such as -COOH, -CO and -OH on the surface of HPPI contributed to the formation of hydrated crystal structures. In general, the oxidation effect of CP effectively improved the hydration properties of HPPI and broaden its application field.

Characterization and correlation of dominant bacteria and volatile compounds in post-fermentation process of Ba-bao Douchi.

Ba-bao Douchi, traditionally produced and spontaneously fermented for 1-2 years, has a unique flavor. However, little information is known about microorganisms and volatile flavors, particularly their relationship. In this study, the aroma profiles including the key aroma compounds, and bacterial communities were characterized and the correlations between dominant bacterial genera with key aroma compounds were studied during the post-fermentation of Ba-bao Douchi. The research showed that 12 bacterial genera were identified as the dominant genus by high-throughput sequencing. A total of 84 volatile compounds were detected by HS-GC-IMS and HS-SPME-GC-MS. Odor activity value (OAV) and gas chromatography-Mass spectrometry-olfactometry (GC-MS-O) were combined to determine the key volatile compounds, and the main volatile compounds including ethyl hexanoate, ethyl heptanoate, isovaleraldehyde, (+)-α-pinene, beta-phellandrene, were found to be responsible for the strong fruitiness, chocolate fragrance, fresh scent flavor, and ginger flavor of Ba-bao Douchi. Pearson correlation analysis showed that 5 dominant bacterial genera were positively associated with > 6 key volatile compounds (p < 0.01, |r| > 0.7). Thus, these bacterial genera might have an effect on the biosynthesis of volatile components. This study provides a theoretical reference for revealing the functional microorganisms and improving the flavor quality of Ba-bao Douchi.

Black mulberry ethanol extract attenuates atherosclerosis-related inflammatory factors and downregulates PPARγ and CD36 genes in experimental atherosclerotic rats.

Atherosclerosis (AS) is the pathological basis of various vascular diseases and currently is seriously affecting human health. Numerous studies have paid more attention to natural medicines with anti-AS properties. As a traditional Uygur folk medicine, black mulberry fruits are conventionally used in the prevention and treatment of cardiovascular diseases in southern Xinjiang of China, and their underlying mechanisms remain unknown. Our previous study revealed that the ethanol extract of black mulberry (EEBM) inhibited AS development by improving lipid metabolism abnormalities, enhancing anti-oxidative activities, and reducing atherosclerotic lesions of atherosclerotic rats. Based on this, our objective was to further investigate the effects of EEBM on the expression of AS-related inflammatory factors and the key genes PPARγ and CD36 of the ox-LDL-PPARγ-CD36 feed-forward cycle in experimental atherosclerotic rats. Black mulberry fruits were extracted with acid ethanol and chromatographed on an AB-8 macroporous resin to obtain EEBM. All experimental rats were randomly divided into five groups: normal, model, model plus simvastatin (5 mg/kg d·body weight), and model plus low-dose and high-dose EEBM groups (105 and 210 mg/kg d·body weight, respectively). Serum levels of the inflammatory factors were determined by enzyme-linked immunosorbent assay (ELISA). The mRNA and protein expression of PPARγ and CD36 in atherosclerotic rats' liver tissue and thoracic aorta were determined by Q-PCR and western blot analysis, respectively. EEBM at high dose effectively attenuated the abnormally expressed AS-related inflammatory factors of TNF-α, IL-6, MMP-9, and CRP in atherosclerotic rats by 41.5%, 66.1%, 77.5%, and 79.5%, respectively. After treatment with high dose EEBM, the elevated-expressions of PPARγ and CD36 at the mRNA and protein levels in atherosclerotic rats were found to be obviously downregulated at both levels. These results demonstrate that EEBM might lessen the AS-related inflammatory reaction, and then inhibit the formation of ox-LDL, consequently downregulating the expression of PPARγ and CD36 at the mRNA and protein levels, thus reducing macrophage-foam-cell formation and prohibiting the development of atherosclerotic plaque through the ox-LDL-PPARγ-CD36 feed-forward cycle, which can effectively prevent the occurrence and development of AS in atherosclerotic rats.

The complete mitochondrial DNA sequence of Yimeng scorpion (Mesobuthus martensii).

Yimeng scorpion is a specific geographical indication breed of Yimeng Mountain area in China. The complete mitochondrial genome sequence of Yimeng scorpion was determined for the first time (Accession number MN597087). It is mitochondrial genome (14,840 bp) contains 13 protein-coding genes, 21tRNA genes, 2 ribosomal RNA genes and one large non-coding region (a possible control region). Moreover, tRNA-ASP-loss was observed from the Yimeng scorpion mitochondrial genome. The mitochondrial genome sequence of the Yimeng scorpion enriches data resource for further research on genetic mechanism and classification.

Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

BACKGROUND: Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. METHODS: The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. RESULTS: A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. CONCLUSIONS: With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.

Site-Directed Genome Knockout in Chicken Cell Line and Embryos Can Use CRISPR/Cas Gene Editing Technology.

The present study established an efficient genome editing approach for the construction of stable transgenic cell lines of the domestic chicken (Gallus gallus domesticus). Our objectives were to facilitate the breeding of high-yield, high-quality chicken strains, and to investigate gene function in chicken stem cells. Three guide RNA (gRNAs) were designed to knockout the C2EIP gene, and knockout efficiency was evaluated in DF-1 chicken fibroblasts and chicken ESCs using the luciferase single-strand annealing (SSA) recombination assay, T7 endonuclease I (T7EI) assay, and TA clone sequencing. In addition, the polyethylenimine-encapsulated Cas9/gRNA plasmid was injected into fresh fertilized eggs. At 4.5 d later, frozen sections of the embryos were prepared, and knockout efficiency was evaluated by the T7EI assay. SSA assay results showed that luciferase activity of the vector expressing gRNA-3 was double that of the control. Results of the T7EI assay and TA clone sequencing indicated that Cas9/gRNA vector-mediated gene knockdown efficiency was approximately 27% in both DF-1 cells and ESCs. The CRISPR/Cas9 vector was also expressed in chicken embryos, resulting in gene knockdown in three of the 20 embryos (gene knockdown efficiency 15%). Taken together, our results indicate that the CRISPR/Cas9 system can mediate stable gene knockdown at the cell and embryo levels in domestic chickens.

A functional temporal association mining approach for screening potential drug-drug interactions from electronic patient databases.

AIMS: Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on spontaneous reports. These methods suffer from severe underreporting, incompleteness, and various bias. The aim of this study was to more effectively screen potential DDIs using patient electronic data and temporal association mining techniques. METHODS: We focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms. We introduced innovative functional temporal association rules where the degree of temporal association between two events within a patient case was defined by a function. RESULTS: Preliminary test results on two drug pairs (i.e., and ) were classified into 260 clinically meaningful categories. These categories were evaluated by physicians and the results exhibited that all the potential DDIs were confined to top 20 of the 260 outcomes. CONCLUSIONS: Our methodology can be used to dramatically reduce a long list of association rules to a manageable list for further analysis and investigation by drug safety professionals.

A temporal interestingness measure for drug interaction signal detection in post-marketing surveillance.

Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on the FDA's (Food and Drug Administration) spontaneous reporting system. However, this system suffers from severe underreporting, which makes it difficult to timely collect enough valid cases for statistical analysis. In this paper, we study how to signal potential DDIs using patient electronic health data. Specifically, we focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms using novel temporal association mining techniques we developed. A new interestingness measure called functional temporal interest was proposed to assess the degrees of temporal association between two drugs of interest and each symptom. The measure was employed to screen potential DDIs from 21,405 electronic patient cases retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The preliminary results indicate the usefulness of our method in finding potential DDIs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.

Modern Computational Techniques for the HMMER Sequence Analysis.

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications-hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.

A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance.

Early detection of unknown adverse drug reactions (ADRs) in postmarketing surveillance saves lives and prevents harmful consequences. We propose a novel data mining approach to signaling potential ADRs from electronic health databases. More specifically, we introduce potential causal association rules (PCARs) to represent the potential causal relationship between a drug and ICD-9 (CDC. (2010). International Classification of Diseases, Ninth Revision (ICD-9). [Online]. Available: http://www.cdc.gov/nchs/icd/icd9.html) coded signs or symptoms representing potential ADRs. Due to the infrequent nature of ADRs, the existing frequency-based data mining methods cannot effectively discover PCARs. We introduce a new interestingness measure, potential causal leverage, to quantify the degree of association of a PCAR. This measure is based on the computational, experience-based fuzzy recognition-primed decision (RPD) model that we developed previously (Y. Ji, R. M. Massanari, J. Ager, J. Yen, R. E. Miller, and H. Ying, "A fuzzy logic-based computational recognition-primed decision model," Inf. Sci., vol. 177, pp. 4338-4353, 2007) on the basis of the well-known, psychology-originated qualitative RPD model (G. A. Klein, "A recognition-primed decision making model of rapid decision making," in Decision Making in Action: Models and Methods, 1993, pp. 138-147). The potential causal leverage assesses the strength of the association of a drug-symptom pair given a collection of patient cases. To test our data mining approach, we retrieved electronic medical data for 16,206 patients treated by one or more than eight drugs of our interest at the Veterans Affairs Medical Center in Detroit between 2007 and 2009. We selected enalapril as the target drug for this ADR signal generation study. We used our algorithm to preliminarily evaluate the associations between enalapril and all the ICD-9 codes associated with it. The experimental results indicate that our approach has a potential to better signal potential ADRs than risk ratio and leverage, two traditional frequency-based measures. Among the top 50 signal pairs (i.e., enalapril versus symptoms) ranked by the potential causal-leverage measure, the physicians on the project determined that eight of them probably represent true causal associations.

A distributed, collaborative intelligent agent system approach for proactive postmarketing drug safety surveillance.

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275,000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.

A team agent approach to postmarketing surveillance of adverse drug reactions.

Current postmarketing surveillance methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare adverse drug reactions (ADRs). We propose an active, multi-agent computer software system, where each agent is empowered with teamwork capabilities such as anticipating information needs, identifying relevant ADR information, and continuously monitoring and proactively sharing such information in a collaborative fashion with other agents. The main purpose of this system is to help regulatory authorities (e.g., FDA in the U.S.) find previously unrecognized ADRs as early as possible. Another objective is to promote increased filing of on-line ADR reports thereby, addressing the severe underreporting problem with the current system. The proposed system has the potential to significantly accelerate the process of ADR discovery and response by utilizing electronic patient data distributed across many different sources and locations more effectively. Our preliminary system design is presented and some issues related to it are discussed.